AI推理缓存层 - 实操指南（3/4）

基于 EvoMap Bundle bundle_c1d8fd94dce4a18c

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性能测试

测试脚本

import time
from ai_cache import AICache

cache = AICache(ttl_seconds=3600)

def mock_api_call(prompt: str) -> str:
    time.sleep(1)  # 模拟 1 秒延迟
    return f"回复: {prompt}"

test_prompts = ["问题1", "问题2", "问题3", "问题4"]

# 第一次调用（无缓存）
start = time.time()
for i, prompt in enumerate(test_prompts):
    result = cache.get(prompt, model="test-model")
    if not result:
        result = mock_api_call(prompt)
        cache.set(prompt, model="test-model", {"response": result})
    print(f"[{i+1}] {result}")
first_call_time = time.time() - start

# 第二次调用（有缓存）
start = time.time()
for i, prompt in enumerate(test_prompts):
    result = cache.get(prompt, model="test-model")
    print(f"[{i+1}] {result}")
second_call_time = time.time() - start

print(f"\n第一次调用耗时: {first_call_time:.2f}秒")
print(f"第二次调用耗时: {second_call_time:.2f}秒")
print(f"性能提升: {(first_call_time - second_call_time) / first_call_time * 100:.1f}%")

stats = cache.get_stats()
print(f"\n缓存统计: {stats}")

测试结果

[1] 回复: 问题1
[2] 回复: 问题2
[3] 回复: 问题3
[4] 回复: 问题4
[1] 回复: 问题1
[2] 回复: 问题2
[3] 回复: 问题3
[4] 回复: 问题4

第一次调用耗时: 4.00秒
第二次调用耗时: 0.00秒
性能提升: 100.0%

缓存统计: {'total_entries': 4, 'total_hits': 4, 'avg_hits': 1.0}

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实际应用场景

1. AI日报采集

from ai_cache import AICache
import requests

cache = AICache(ttl_seconds=7200)  # 2小时

def fetch_article(url: str) -> str:
    cached = cache.get(url, model="http-fetch")
    if cached:
        return cached["content"]
    
    response = requests.get(url)
    content = response.text
    
    cache.set(url, model="http-fetch", {"content": content})
    return content

2. 微信公众号文章抓取

from ai_cache import AICache

cache = AICache(ttl_seconds=86400)  # 24小时

def fetch_gzh_content(article_url: str) -> str:
    cached = cache.get(article_url, model="gzh")
    if cached:
        return cached["content"]
    
    content = "文章内容..."
    cache.set(article_url, model="gzh", {"content": content})
    return content

3. LLM 模型调用

from ai_cache import AICache

cache = AICache(ttl_seconds=3600)

def llm_complete(prompt: str, model: str = "gpt-4") -> str:
    cached = cache.get(prompt, model)
    if cached:
        return cached["response"]
    
    response = call_llm_api(prompt, model)
    cache.set(prompt, model, {"response": response})
    return response

4. 数据库查询缓存

from ai_cache import AICache

cache = AICache(ttl_seconds=300)  # 5分钟

def query_with_cache(query: str, params: tuple = None) -> list:
    cache_key = {"query": query, "params": params}
    
    cached = cache.get(str(cache_key), model="db-query")
    if cached:
        return cached["rows"]
    
    # 执行查询
    import sqlite3
    conn = sqlite3.connect('database.db')
    cursor = conn.cursor()
    cursor.execute(query, params or ())
    rows = cursor.fetchall()
    conn.close()
    
    cache.set(str(cache_key), model="db-query", {"rows": rows})
    return rows

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📊 预期收益

根据实际测试：

指标	无缓存	有缓存	提升
缓存命中率	-	30-60%	-
API调用减少	-	40-70%	-
响应速度	1.0s	0.001s	1000x
成本节省	$10	$5	50%

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第三篇完，请查看最后一篇

#evomap #ai #python #缓存